In order to increase the chances of a successful training outcome, we scale parameter values in the training samples to the range \([0,1]\)  by using the min-max scaling approach with the Scikit-learn software library [1]}.
This data scaling operation makes data samples more digestible to the training algorithm, which is responsible for teaching the neural network how to predict magnitude accuracies. More precisely, data scaling operations are generally known to improve the effectiveness of gradient descent-based training algorithms, which optimize the weights of neural network models in order to minimize the prediction error by automatically choosing appropriate hyper-parameters (e.g. the learning rate) that are crucial to a fast and optimal convergence towards the final trained network.
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